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高压开关柜热故障预警策略及其应用
引用本文:黄新波,薛智鹏,田毅,蒋波涛,陈丽.高压开关柜热故障预警策略及其应用[J].电力自动化设备,2019,39(7).
作者姓名:黄新波  薛智鹏  田毅  蒋波涛  陈丽
作者单位:西安工程大学电子信息学院,陕西西安,710048;西安翔腾微电子技术有限公司,陕西西安,710068
基金项目:陕西省重点科技创新团队计划项目(2014KCT-16);陕西省工业科技攻关项目(2016GY-052);陕西省重点研发计划项目(2018ZDXM-GY-040)
摘    要:依据高压开关柜热故障的时域多样性,对于长期故障和即时故障,分别提出了改进的组合权重相似日方法和改进的等效电阻模型。改进的组合权重相似日方法利用熵权法及序关系分析法完成相似日的求取及权重系数的分配,避免了相似日求取的不合理问题,有效地提高了预测精度。改进的等效电阻模型嵌入动态阈值算法及空间相关分析法处理奇异点数据和特征信号,使故障信号分析更加准确。实验结果表明,所提方法和模型能够准确有效地预测载流故障发展的整体趋势,精度较高。

关 键 词:高压开关柜  热故障  空间相关分析法  熵权法  相似日  等效电阻
收稿时间:2018/11/8 0:00:00
修稿时间:2019/5/16 0:00:00

Early thermal fault warning strategy of high voltage switch cabinet and its application
HUANG Xinbo,XUE Zhipeng,TIAN Yi,JIANG Botao and CHEN Li.Early thermal fault warning strategy of high voltage switch cabinet and its application[J].Electric Power Automation Equipment,2019,39(7).
Authors:HUANG Xinbo  XUE Zhipeng  TIAN Yi  JIANG Botao and CHEN Li
Affiliation:College of Electronics and Information, Xi''an Polytechnic University, Xi''an 710048, China,College of Electronics and Information, Xi''an Polytechnic University, Xi''an 710048, China,College of Electronics and Information, Xi''an Polytechnic University, Xi''an 710048, China,College of Electronics and Information, Xi''an Polytechnic University, Xi''an 710048, China and Xi''an Xiangteng Microelectronics Technology Co.,Ltd.,Xi''an 710068, China
Abstract:Based on the time-domain diversity of thermal faults of high voltage switch cabinet, an improved combined weight similarity day method is proposed for long-term failure prediction, while an improved equivalent resistance model is established for real-time fault prediction. The improved combined weight similarity day method adopts the entropy weight method and order relationship analysis method to complete the calculation of similar days and the allocation of weight coefficients, which avoids the irrationality problem of similar day calculation and effectively improves the prediction accuracy. The improved equivalent resistance model is embedded with the dynamic threshold algorithm and spatial correlation analysis method to deal with the singularity data processing and feature extraction, which makes the fault signal analysis more accurate. The experimental results show that the proposed method and model can effectively predict the overall trend of faulty development in the early stage of current carrying faults with high accuracy.
Keywords:high voltage switch cabinet  thermal fault  spatial correlation analysis method  entropy weight method  similar day  equivalent resistance
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